Transfer learning helps in alleviating the problem of limited data. However, it is computationally expensive to quantify uncertainty from single models [1]}. Thus, a combination of two factors, the necessity for pre-trained networks due to the limited size of manually annotated training data and the ease of implementing UQ using an ensemble approach, motivated our choice of pre-trained ensembles. In this work, we have implemented an ensemble of pre-trained neural networks to implement a workflow for accurate phase segmentation from transmission electron microscopy (TEM) datasets. The specific objectives for the ensemble are as follows: 1) perform accurate segmentation; 2) demonstrate good calibration within the training data domain; and 3) reliably quantify the uncertainty for new data inputs. The use of ensemble of neural networks for ML applications is a relatively well-established field. A review of ensemble learning approaches, in the context of neural networks, can be found in, e.g., [2]}, [3]}, etc. One of the important criteria to consider while designing an ensemble is the diversity among its members. There are multiple ways to incorporate this diversity [4]}, such as by using different starting points through random initialization, different architectures for individual models, different training data subsets, etc. In this work, the utility of two different ensemble designs were tested towards fulfilling the stated objectives. The first ensemble (Ensemble of Architectures, or 'EA') consisted of pre-trained neural network of different model architectures, whereas the second set of ensembles (Ensemble of random initialization of architecture 'i', or ER-i) consisted of pre-trained neural networks of identical architecture 'i'.
